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These studies are available for the following degree students:</p><ul><li>Bachelor's Degree Programme in Mathematics</li><li>Master's Degree Programme in Mathematics</li><li>Bachelor's Degree Programme in Mathematics (Subject Teacher)</li><li>Master's Degree Programme in Mathematics&nbsp;(Subject Teacher)</li><li>Bachelor's Degree Programme in Mathematics, Chemistry or Physics Subject Teacher Education and Primary Teacher Education (Specialication in Mathematics)</li><li>Master's Degree Programme in Mathematics, Chemistry or Physics Subject Teacher Education and Primary Teacher Education (Specialication in Mathematics)</li><li>Doctoral Programme in Mathematics and Statistics</li><li>Doctoral Programme in Mathematics and Science (Specialication in Mathematics)</li><br></ul>","fi":"<p>Tämä opintojakso on tarjolla Matematiikan syventävät opinnot -ristiinopiskeluverkostossa. Verkoston opinnot ovat tarjolla seuraaville opiskelijoille:</p><ul><li>Matematiikan kandidaattiohjelma</li><li>Matematiikan maisteriohjelma</li><li>Matematiikan aineenopettajien kandidaattiohjelma</li><li>Matematiikan aineenopettajien maisteriohjelma</li><li>Matematiikan, kemian tai fysiikan aineenopettajan ja luokanopettajan kandidaattiohjelma (matematiikan opintosuunta)</li><li>Matematiikan, kemian tai fysiikan aineenopettajan ja luokanopettajan maisteriiohjelma (matematiikan opintosuunta)</li><li>Matematiikan ja tilastotieteen tohtoriohjelma</li><li>Matemaattisten tieteiden ja luonnontieteiden tohtoriohjelma (matematiikan opintosuunta)</li><br></ul>"},"cooperationNetwork":{"abbreviation":"matematiikansyventavat","name":{"en":"Cross-institutional studies in advanced courses in mathematics and statistics","fi":"Matematiikan ja tilastotieteen syventävien kurssien ristiinopiskelu","sv":"Korsstudier i fördjupade kurser i matematik och statistik"}}}],"gradeScaleId":"sis-0-5","outcomes":{"en":"By the end of this course students will be able to formulate the typical machine learning problems as optimisation problems and will understand the mathematical foundations of the key solutions in the practice of machine learning.","fi":"Kurssin päätteeksi opiskelija osaa muotoilla tyypilliset koneoppimisongelmat optimointitehtäviksi ja ymmärtää keskeisten ratkaisujen matemaattiset perusteet koneoppimisen käytännössä."},"tweetText":null,"content":{"en":"This course (in 3 parts) is a general introduction to machine learning focusing on the fundamental modern topics in this field and providing the theoretical bases and concepts behind key algorithms. The course aims to provide a deep understanding of the nature of the problems addressed in machine learning and of the computational strategies behind the most popular approaches in this field. The topics we cover include design and analysis of machine learning experiments, supervised learning, unsupervised learning, active learning, reinforcement learning, Bayesian decision theory, parametric methods, multivariate methods, multilayer perceptrons, local models, hidden Markov models, kernel machines, graphical models. Short programming assignments include hands-on experiments with various learning algorithms.","fi":"Tämä kurssi (3 osassa) on yleinen johdatus koneoppimiseen keskittyen tämän alan perusaiheisiin ja tarjoaa teoreettiset perusteet ja käsitteet avainalgoritmien takana. Kurssin tavoitteena on antaa syvällinen ymmärrys koneoppimisen ongelmien luonteesta ja alan suosituimpien lähestymistapojen taustalla olevista laskentastrategioista."},"additional":{"en":"Lectures are not given every year but it&#39;s also possible to take the course independently. Academic year 2026-2026 available only for independent study.","fi":"Opintojaksoa ei luennoida säännöllisesti, mutta sen voi suorittaa myös itsenäisesti. Lukuvuonna 2026-2027 tarjolla vain itsenäisenä suorituksena."},"prerequisites":{"en":"Maturity in computer programming, probability, calculus, and linear algebra.","fi":"Kypsyys tietokoneohjelmointiin, todennäköisyyslaskentaan, laskemiseen ja lineaarialgebraan."},"compulsoryFormalPrerequisites":[],"recommendedFormalPrerequisites":[],"literature":[],"learningMaterial":null,"completionMethods":[]}],"prerequisiteCourseUnit":[],"prerequisiteModule":[]},"prerequisiteCourseUnitPage":{"nodes":[]},"prerequisiteModulePage":{"nodes":[]},"parentModulePage":{"nodes":[]}},"pageContext":{"type":"courseUnit","locale":"en","title":"Foundations of Machine Learning I","id":"otm-d7eb6b59-24aa-3935-9bcd-1c88169daaa6","code":"MATE5427","prerequisiteCourseUnitIds":[],"prerequisiteModuleIds":[],"parentModuleIds":[],"curriculumPeriodStartDate":"2026-08-01","curriculumPeriodEndDate":"2027-08-01","coordinatingOrgIds":[],"searchable":false,"searchTags":null,"organisationIds":["otm-69f8ea31-de0c-3050-ab2a-e571fbab1f95"],"organisations":[],"attainmentLanguages":[],"hasSummerStudies":false,"teachingPeriods":[],"cooperationNetworkDirection":"INBOUND","hasCooperationNetworkSettings":true,"hasAvoinTeaching":false}}}